After AI tools enter the writing process, "academic integrity" goes from a slogan to an actionable question: what exactly should be checked? By what standards?
This article breaks the integrity self-check into five questions. muchsci has mechanism fallbacks for the first four; the fifth can only ever be answered by you.
Question One: Is the data real?
What to check: every number has a source, with no placeholder residue.
Mechanism fallback: the first iron rule of muchsci Thesis Sailor—without real data, do not write the experiment/results section; only provide a checklist of "what is missing"; target-value placeholders are never automatically filled with numbers. The tool makes it hard for you to fabricate data without knowing it.
Question Two: Are the citations real?
What to check: every entry passes three-way cross-checking and authenticity verification.
Mechanism fallback: only real entries returned by retrieval and ingestion are eligible for the reference list; fabricated citations cannot enter the reference list—hallucinated references are stopped at the entrance.
Question Three: Is the language version consistent?
What to check: no mixed Chinese-English strings anywhere in the manuscript; the delivery version matches the language you declared.
Mechanism fallback: the Chinese and English production lines are physically isolated, and output directories and file names are archived by language—version mixing cannot occur at the mechanism level.
Question Four: Has the AI flavor been cleaned out?
What to check: all high items in the polish index report have been properly rewritten.
Mechanism fallback: the rule engine detects clichéd transitions, empty strong assertions, and translationese, locating each one and rewriting it to zero—not to game detection, but so the text is worthy of your ideas.
Question Five: Are the ideas mine?
What to check: you can fully restate the main line of argument yourself.
There is no mechanism fallback for this question. Tools can verify citations, flag data gaps, and clean up expression, but "are the ideas in this paper yours?"—only you can answer that. If you can no longer explain the main line of argument clearly, the severity of the problem exceeds the sum of the first four questions.
The tool's integrity design mapped to each question
| muchsci's integrity design | Which question it corresponds to |
| --- | --- |
| No-fabrication iron rule: without real data, do not write the results section | Question One |
| Citation authenticity verification: fabricated entries cannot enter the reference list | Question Two |
| Chinese-English dual production lines: language version is hard-coded | Question Three |
| Remove AI flavor: rewrite high items to zero | Question Four |
Four questions have mechanism fallbacks; the fifth is always you. A good tool does not help you bypass integrity checks; it makes it hard for you to go bad by accident—hand what can be checked to mechanisms, and keep what cannot be outsourced to yourself. That is the complete form of academic integrity in the AI era.